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load libraries


### https://cran.r-project.org/web/packages/udpipe/vignettes/udpipe-usecase-postagging-lemmatisation.html
library(udpipe)
ud_model <- udpipe_download_model(language = "english")
library(tidyverse)
library(tidyr)
library(dplyr)
library(ggplot2)
library(ggrepel)
library(knitr)
library(tm)
library(quanteda)
library(lattice)
library(latticeExtra)
library(plotly)
library(pdp)
library(patchwork)

load the FW functions


### CODE DIRECTLY FROM: https://burtmonroe.github.io/TextAsDataCourse/Tutorials/TADA-FightinWords.nb.html#

fwgroups <- function(dtm, groups, pair = NULL, weights = rep(1,nrow(dtm)), k.prior = .1) {
  
  weights[is.na(weights)] <- 0
  
  weights <- weights/mean(weights)
  
  zero.doc <- rowSums(dtm)==0 | weights==0
  zero.term <- colSums(dtm[!zero.doc,])==0
  
  dtm.nz <- apply(dtm[!zero.doc,!zero.term],2,"*", weights[!zero.doc])
  
  g.prior <- tcrossprod(rowSums(dtm.nz),colSums(dtm.nz))/sum(dtm.nz)
  
  # 
  
  g.posterior <- as.matrix(dtm.nz + k.prior*g.prior)
  
  groups <- groups[!zero.doc]
  groups <- droplevels(groups)
  
  g.adtm <- as.matrix(aggregate(x=g.posterior,by=list(groups=groups),FUN=sum)[,-1])
  rownames(g.adtm) <- levels(groups)
  
  g.ladtm <- log(g.adtm)
  
  g.delta <- t(scale( t(scale(g.ladtm, center=T, scale=F)), center=T, scale=F))
  
  g.adtm_w <- -sweep(g.adtm,1,rowSums(g.adtm)) # terms not w spoken by k
  g.adtm_k <- -sweep(g.adtm,2,colSums(g.adtm)) # w spoken by groups other than k
  g.adtm_kw <- sum(g.adtm) - g.adtm_w - g.adtm_k - g.adtm # total terms not w or k 
  
  g.se <- sqrt(1/g.adtm + 1/g.adtm_w + 1/g.adtm_k + 1/g.adtm_kw)
  
  g.zeta <- g.delta/g.se
  
  g.counts <- as.matrix(aggregate(x=dtm.nz, by = list(groups=groups), FUN=sum)[,-1])
  
  if (!is.null(pair)) {
    pr.delta <- t(scale( t(scale(g.ladtm[pair,], center = T, scale =F)), center=T, scale=F))
    pr.adtm_w <- -sweep(g.adtm[pair,],1,rowSums(g.adtm[pair,]))
    pr.adtm_k <- -sweep(g.adtm[pair,],2,colSums(g.adtm[pair,])) # w spoken by groups other than k
    pr.adtm_kw <- sum(g.adtm[pair,]) - pr.adtm_w - pr.adtm_k - g.adtm[pair,] # total terms not w or k
    pr.se <- sqrt(1/g.adtm[pair,] + 1/pr.adtm_w + 1/pr.adtm_k + 1/pr.adtm_kw)
    pr.zeta <- pr.delta/pr.se
    
    return(list(zeta=pr.zeta[1,], delta=pr.delta[1,],se=pr.se[1,], counts = colSums(dtm.nz), acounts = colSums(g.adtm)))
  } else {
    return(list(zeta=g.zeta,delta=g.delta,se=g.se,counts=g.counts,acounts=g.adtm))
  }
}

############## FIGHTIN' WORDS PLOTTING FUNCTION

# helper function
makeTransparent<-function(someColor, alpha=100)
{
  newColor<-col2rgb(someColor)
  apply(newColor, 2, function(curcoldata){rgb(red=curcoldata[1], green=curcoldata[2],
                                              blue=curcoldata[3],alpha=alpha, maxColorValue=255)})
}

fw.ggplot.groups <- function(fw.ch, groups.use = as.factor(rownames(fw.ch$zeta)), max.words = 50, max.countrank = 400, colorpalette=rep("black",length(groups.use)), sizescale=2, title="Comparison of Terms by Groups", subtitle = "", caption = "Group-specific terms are ordered by Fightin' Words statistic (Monroe, et al. 2008)") {
  if (is.null(dim(fw.ch$zeta))) {## two-group fw object consists of vectors, not matrices
    zetarankmat <- cbind(rank(-fw.ch$zeta),rank(fw.ch$zeta))
    colnames(zetarankmat) <- groups.use
    countrank <- rank(-(fw.ch$counts))
  } else {
    zetarankmat <- apply(-fw.ch$zeta[groups.use,],1,rank)
    countrank <- rank(-colSums(fw.ch$counts))
  }
  wideplotmat <- as_tibble(cbind(zetarankmat,countrank=countrank))
  wideplotmat$term=names(countrank)
  #rankplot <- gather(wideplotmat, party, zetarank, 1:ncol(zetarankmat))
  rankplot <- gather(wideplotmat, groups.use, zetarank, 1:ncol(zetarankmat))
  rankplot$plotsize <- sizescale*(50/(rankplot$zetarank))^(1/4)
  rankplot <- rankplot[rankplot$zetarank < max.words + 1 & rankplot$countrank<max.countrank+1,]
  rankplot$groups.use <- factor(rankplot$groups.use,levels=groups.use)
  
  p <- ggplot(rankplot, aes((nrow(rankplot)-countrank)^1, -(zetarank^1), colour=groups.use)) + 
    geom_point(show.legend=F,size=sizescale/2) + 
    theme_classic() +
    theme(axis.ticks=element_blank(), axis.text=element_blank() ) +
    ylim(-max.words,40) +
    facet_grid(groups.use ~ .) +
    geom_text_repel(aes(label = term), size = rankplot$plotsize, point.padding=.05,
                    box.padding = unit(0.20, "lines"), show.legend=F, max.overlaps = Inf) +
    scale_colour_manual(values = alpha(colorpalette, .7)) + 
#    labs(x="Terms used more frequently overall →", y="Terms used more frequently by group →",  title=title, subtitle=subtitle , caption = caption) 
    labs(x=paste("Terms used more frequently overall -->"), y=paste("Terms used more frequently by group -->"),  title=title, subtitle=subtitle , caption = caption) 
  
}

options(ggrepel.max.overlaps = Inf)

fw.keys <- function(fw.ch,n.keys=10) {
  n.groups <- nrow(fw.ch$zeta)
  keys <- matrix("",n.keys,n.groups)
  colnames(keys) <- rownames(fw.ch$zeta)
  
  for (g in 1:n.groups) {
    keys[,g] <- names(sort(fw.ch$zeta[g,],dec=T)[1:n.keys])
  }
  keys
}

Compare Associated Press 1994-2010: Before and After “extremist” and other query terms

Query search: (activi* | ahbash | akromiya | anjem | ansharut | anticapital* | antidemocr* | antiestablish* | antifa | antigovern* | antimilitar* | antimonarch* | antipatri* | antireli* | antisem* | antisocia* | antisyst* | apost* | atharis | athei* | atheists | außerparlamentari* | authoritar* | bagau* | bigots | bplf | bukhari* | capitulatio* | conspirato* | counterj* | cybercalip* | damigo | dawro* | demon* | deradicaliza* | deviatio* | diqqi | dissid* | djamaat | dotbus* | ecofas* | espou* | ethnonationa* | extrem* | facists | fadaia* | fanat* | fasci* | fetö | fightdem* | freedo* | fundamental* | fuqra | gafatar | gamerga* | gemidzii | ghuluww | globali* | gramsc* | gülen* | hacktiv* | haquna | hardline | harkatul | hatemon* | heimwe* | hezbol* | hinduph* | hindutva | hizbut | hojja* | ideolog* | incitem* | inciters | insurr* | intacti* | islam4uk | islam* | jaljalat | jbakc | jihadi* | jmjb | jrtn | judai* | juhayman | jundu* | kadiza* | kahanism | kahanist | karram* | kaysa* | khalis* | khatmia | khawarij | khomein* | koutla | leftist | leftists | leftwing | liberatio* | madkha* | madkhal* | maimonid* | manosp* | mauras* | mcln | militan* | millatu | monarc* | mudja* | muhaji* | mujahid* | murab* | muttahi* | najjadah | nationali* | neofas* | neona* | opantish | oppositi* | paleolibertar* | paramili* | parliamenta* | pegida | populist | principa* | profe* | prowar | putinist | qadari | quranism | quranist | qutbism | qutbist | qutbists | racis* | radica* | reactioni* | reformis* | reichsbürgerbewe* | rightist | rightw* | rofiq | russoph* | sabireen | sadda* | salaf* | sayaff | scriptura* | secula* | separationi* | sharia4hol* | sikrikim | split* | squadism | strasse* | subver* | suidlan* | sukarn* | suprema* | supremac* | sympathi* | table* | tabliq | takfir | takfir* | takfi* |terror* | theoc* | titoite | triba* | trots* | trotsk* | ukrainoph* | ultraconserva* | ultralib* | ultranationa* | ultrar* | uscmo | wahabbi | wahab* | wahha* | xenoph* | yulde* | zinovie*)

Load and clean the data

text_cleaner<-function(corpus){
  tempcorpus<-Corpus(VectorSource(corpus))
  tempcorpus<-tm_map(tempcorpus,
                    removePunctuation)
  tempcorpus<-tm_map(tempcorpus,
                    stripWhitespace)
  tempcorpus<-tm_map(tempcorpus,
                    removeNumbers)
  tempcorpus<-tm_map(tempcorpus,
                     removeWords, stopwords("english"))
  tempcorpus<-tm_map(tempcorpus, 
                    stemDocument)
  return(tempcorpus)
}

Calculate FW.


e <- dfm(extremecorpus$content)
message(dim(e))
head(e)
Document-feature matrix of: 6 documents, 14,891 features (100.0% sparse).
       features
docs    us struggl uptick american plot attack past month last week
  text1  1       1      1        1    1      0    0     0    0    0
  text2  0       0      0        0    0      1    1     1    1    1
  text3  0       0      0        0    0      0    0     0    0    0
  text4  0       0      0        0    0      0    0     0    0    0
  text5  0       0      0        0    0      0    0     0    0    0
  text6  0       0      0        0    0      0    0     0    0    0
[ reached max_nfeat ... 14,881 more features ]
#############################################

e <- dfm_select(e, pattern = stopwords("english"), selection = "remove")
e <- dfm_select(e, min_nchar = 2)
e <- dfm_trim(e, min_termfreq = 4, min_docfreq = .05, verbose=TRUE)

#dim(e)
# sparsity(e)

#############################################

extrem_dtm <- convert(e, to='data.frame')
extrem_dtm <- extrem_dtm[-c(1)]
w <- which( sapply(extrem_dtm, class ) == 'character' )

#############################################

fw.extrem <- fwgroups(extrem_dtm, groups=extrem_NYT.dfm.long$Context)

Get and show the top words per group by zeta.

Context.before Context.after
islam group
palestinian hussein
war attack
iraqi parti
suspect elect
crack leader
jemaah jihad
crackdown milit
main movement
alleg said
support guerrilla
evid network
albanian regim
prodemocraci armi
sept politician
muslim organ
megawati outsid
battl univers
prevent bomb
kosovo rocket

Plot: Before in Blue, After in Red

Calculate by query item/search term


extrem_dtm_topn <- convert(e, to='data.frame')
extrem_dtm_topn$Number.of.hit <- extrem_NYT.dfm.long$Number.of.hit
extrem_dtm_topn$Context <- extrem_NYT.dfm.long$Context

topn_terms <- extrem_NYT.dfm.long %>%
      filter(Query.item %in% top_n$term)

extrem_dtm_topn_keep <- extrem_dtm_topn %>% 
    filter(Number.of.hit %in% topn_terms$Number.of.hit)

extrem_dtm_topn_keep_before <- extrem_dtm_topn_keep[grep("before",extrem_dtm_topn_keep$Context),]
extrem_dtm_topn_keep_after <- extrem_dtm_topn_keep[grep("after", extrem_dtm_topn_keep$Context),]

rr <-dim(topn_terms)[1]
r <- sum(length(extrem_dtm_topn_keep_before))
topn_terms_before <- topn_terms[seq(1,rr,2),]
topn_terms_after <- topn_terms[seq(2,rr,2),]

extrem_dtm_topn_keep_before <- extrem_dtm_topn_keep_before[-c(1, r-1, r)]
extrem_dtm_topn_keep_after <- extrem_dtm_topn_keep_after[-c(1, r-1, r)]

rm(rr)
rm(r)
rm(extrem_dtm_topn)

#############################################
fw.query_item_before <- fwgroups(extrem_dtm_topn_keep_before,groups = topn_terms_before$Query.item)
fwkeys.query_item_before <- fw.keys(fw.query_item_before, n.keys=15)
kable(fwkeys.query_item_before, caption = "Top 15 Words for Query Term: BEFORE")
Top 15 Words for Query Term: BEFORE
Islamic militants opposition Saddam terrorist
milit islam main iraqi sept
organ palestinian elect presid alqaida
strict kill polit iraq bin
radic suspect parliament war laden
somalia attack opposit saddam involv
hama taliban parti oust unit
secular isra minist baghdad suspect
hardlin muslim vote fall link
iran gaza democrat bush state
turkey armi prime hussein consid
extremist troop strong captur connect
malaysia israel percent regim appar
suprem pakistan despit usl osama
gaza forc poll former charg
fundamentalist arafat voic death list

p.fw.query_item_before <- fw.ggplot.groups(fw.query_item_before,sizescale=2,max.words=150,max.countrank=400,
                                           colorpalette = c('blue','blue','blue', 'blue','blue'),
                                           title = 'Comparison of Terms by Overall Top Terms: BEFORE')
p.fw.query_item_before


#############################################
fw.query_item_after <- fwgroups(extrem_dtm_topn_keep_after,groups = topn_terms_after$Query.item)
fwkeys.query_item_after <- fw.keys(fw.query_item_after, n.keys=15)
kable(fwkeys.query_item_after, caption = "Top 15 Words for Query Term: AFTER")
Top 15 Words for Query Term: AFTER
Islamic militants opposition Saddam terrorist
milit isra parti hussein attack
group israel leader regim organ
jihad kashmir democrat iraq state
hama gaza vote iraqi unit
movement palestinian parliament un activ
law kill lawmak weapon group
insurg attack protest saddam act
extremist region elect son network
front southern candid captur bomb
revolut fire politician kuwait suspect
republ rocket coalit mass threat
court pakistani social baghdad financ
fundamentalist fight conserv us sept
news border win resolut link
agenc area opposit palac cell

p.fw.query_item_after <- fw.ggplot.groups(fw.query_item_after,sizescale=2,max.words=150,max.countrank=400,
                                           colorpalette = c('red', 'red','red','red','red'),
                                          title = 'Comparison of Terms by Overall Top Terms: AFTER')
p.fw.query_item_after

NA
NA

Calculate Parts of speech by before and after

Calculate FW and keys


ud_model <- udpipe_load_model(ud_model$file_model)

txt <-as.character(extrem_NYT.dfm.long$context.text)

x_udp <- udpipe_annotate(ud_model, x = txt, doc_id = seq_along(txt))
x <- as.data.frame(x_udp)

x$doc_id <-as.integer(x$doc_id)

x_odd.before <- x[x$doc_id %% 2 == 1,]
x_even.after <-x[x$doc_id %% 2 == 0, ]

A few barchart functions


## UNIVERSAL PoS
UPOS_barchart <- function(df1, df2){
  stats1 <- txt_freq(df1$upos)
  stats1$key <- factor(stats1$key, levels = rev(stats1$key))
  
  stats2 <- txt_freq(df2$upos)
  stats2$key <- factor(stats2$key, levels = rev(stats2$key))
  
  c(barchart(key ~ freq, data = stats1, col = "cadetblue", 
        main = "UPOS (Universal Parts of Speech)\n frequency of occurrence: BEFORE vs AFTER", 
         xlab = "Freq"), 
    barchart(key ~ freq, data = stats2, col =  'skyblue',
         xlab = "Freq"))
}



## NOUNS
NOUNS_barchart <- function(df1, df2){
  
  stats1 <- subset(df1, upos %in% c("NOUN")) 
  stats1 <- txt_freq(stats1$token)
  stats1$key <- factor(stats1$key, levels = rev(stats1$key))
  
  stats2 <- subset(df2, upos %in% c("NOUN")) 
  stats2 <- txt_freq(stats2$token)
  stats2$key <- factor(stats2$key, levels = rev(stats2$key))
  
  c(barchart(key ~ freq, data = head(stats1, 20), col = "cadetblue", 
           main = "Most occurring nouns: BEFORE vs AFTER", xlab = "Freq"),
      barchart(key ~ freq, data = head(stats2, 20), col = "skyblue", 
            xlab = "Freq"))
}

## ADJECTIVES
ADJ_barchart <- function(df1, df2){
  
  stats1 <- subset(df1, upos %in% c("ADJ")) 
  stats1 <- txt_freq(stats1$token)
  stats1$key <- factor(stats1$key, levels = rev(stats1$key))
  
  stats2 <- subset(df2, upos %in% c("ADJ")) 
  stats2 <- txt_freq(stats2$token)
  stats2$key <- factor(stats2$key, levels = rev(stats2$key))
  
  c(barchart(key ~ freq, data = head(stats1, 20), col = "cadetblue", 
           main = "Most occurring adjectives: BEFORE vs AFTER", xlab = "Freq"),
      barchart(key ~ freq, data = head(stats2, 20), col = "skyblue", 
         xlab = "Freq"))
}

## Using RAKE to find keywords
RAKE_KW_barchart <- function(df1,df2){
  
  stats1 <- keywords_rake(x = df1, term = "lemma", group = "doc_id", 
                         relevant = df1$upos %in% c("NOUN", "ADJ"))
  stats1$key <- factor(stats1$keyword, levels = rev(stats1$keyword))
  
  stats2 <- keywords_rake(x = df2, term = "lemma", group = "doc_id", 
                         relevant = df2$upos %in% c("NOUN", "ADJ"))
  stats2$key <- factor(stats2$keyword, levels = rev(stats2$keyword))
  
  
  c(barchart(key ~ rake, data = head(subset(stats1, freq > 3), 20), col = "cadetblue", 
           main = "Keywords identified by RAKE: BEFORE vs AFTER", 
           xlab = "Rake"),
    barchart(key ~ rake, data = head(subset(stats2, freq > 3), 20), col = "skyblue", 
           xlab = "Rake"))
}

## Using Pointwise Mutual Information Collocations
PWI_barchart <- function(df1, df2){
  
  df1$word <- tolower(df1$token)
  stats1 <- keywords_collocation(x = df1, term = "word", group = "doc_id")
  stats1$key <- factor(stats1$keyword, levels = rev(stats1$keyword))
  
  df2$word <- tolower(df2$token)
  stats2 <- keywords_collocation(x = df2, term = "word", group = "doc_id")
  stats2$key <- factor(stats2$keyword, levels = rev(stats2$keyword))
  
  c(barchart(key ~ pmi, data = head(subset(stats1, freq > 3), 20), col = "cadetblue", 
           main = "Keywords identified by PMI Collocation: BEFORE vs AFTER", 
           xlab = "PMI (Pointwise Mutual Information)"),
      barchart(key ~ pmi, data = head(subset(stats2, freq > 3), 20), col = "skyblue", 
           xlab = "PMI (Pointwise Mutual Information)"))
}

## Using a sequence of POS tags (noun phrases / verb phrases)
POS_barchart <- function(df1, df2){
  
  df1$phrase_tag <- as_phrasemachine(df1$upos, type = "upos")
  stats1 <- keywords_phrases(x = df1$phrase_tag, term = tolower(df1$token), 
                            pattern = "(A|N)*N(P+D*(A|N)*N)*", 
                            is_regex = TRUE, detailed = FALSE)
  stats1 <- subset(stats1, ngram > 1 & freq > 3)
  stats1$key <- factor(stats1$keyword, levels = rev(stats1$keyword))
  
  df2$phrase_tag <- as_phrasemachine(df2$upos, type = "upos")
  stats2 <- keywords_phrases(x = df2$phrase_tag, term = tolower(df2$token), 
                            pattern = "(A|N)*N(P+D*(A|N)*N)*", 
                            is_regex = TRUE, detailed = FALSE)
  stats2 <- subset(stats2, ngram > 1 & freq > 3)
  stats2$key <- factor(stats2$keyword, levels = rev(stats2$keyword))
  
  c(barchart(key ~ freq, data = head(stats1, 20), col = "cadetblue", 
           main = "Keywords - simple noun phrases: BEFORE vs AFTER", xlab = "Frequency"),
      barchart(key ~ freq, data = head(stats2, 20), col = "skyblue", 
               xlab = "Frequency"))
}

Bar Charts from Functions Above


UPOS_barchart(x_odd.before, x_even.after)

NOUNS_barchart(x_odd.before, x_even.after)

ADJ_barchart(x_odd.before, x_even.after)

RAKE_KW_barchart(x_odd.before, x_even.after)

PWI_barchart(x_odd.before, x_even.after)

POS_barchart(x_odd.before, x_even.after)

Cooccurences


CO_OC_noun_adj_same_sent.before <- function(df1){
  
  library(igraph)
  library(ggraph)
  library(ggplot2)
  
  cooc <- cooccurrence(x = subset(df1, upos %in% c("NOUN", "ADJ")), 
                       term = "lemma", 
                       group = c("doc_id", "paragraph_id", "sentence_id"))

  wordnetwork <- head(cooc, 60)
  wordnetwork <- graph_from_data_frame(wordnetwork)
  
  ggraph(wordnetwork, layout = "fr") +
    geom_edge_link(aes(width = cooc, edge_alpha = cooc), edge_colour = "pink") +
    geom_node_text(aes(label = name), col = "darkgreen", size = 4) +
    theme_graph(base_family = "Arial Narrow") +
    theme(legend.position = "none") +
    labs(title = "Cooccurrences within sentence: BEFORE", subtitle = "Nouns & Adjective")
  
}

CO_OC_noun_adj_same_sent.after <- function(df2){
  
  library(igraph)
  library(ggraph)
  library(ggplot2)
  
  cooc <- cooccurrence(x = subset(df2, upos %in% c("NOUN", "ADJ")), 
                       term = "lemma", 
                       group = c("doc_id", "paragraph_id", "sentence_id"))

  wordnetwork <- head(cooc, 60)
  wordnetwork <- graph_from_data_frame(wordnetwork)
  
  ggraph(wordnetwork, layout = "fr") +
    geom_edge_link(aes(width = cooc, edge_alpha = cooc), edge_colour = "lightgreen") +
    geom_node_text(aes(label = name), col = "darkblue", size = 4) +
    theme_graph(base_family = "Arial Narrow") +
    theme(legend.position = "none") +
    labs(title = "Cooccurrences within sentence: AFTER", subtitle = "Nouns & Adjective")
  
}


CO_OC_noun_adj_same_sent.before(x_odd.before)

Attaching package: ‘igraph’

The following object is masked from ‘package:plotly’:

    groups

The following object is masked from ‘package:quanteda’:

    as.igraph

The following objects are masked from ‘package:dplyr’:

    as_data_frame, groups, union

The following objects are masked from ‘package:purrr’:

    compose, simplify

The following object is masked from ‘package:tidyr’:

    crossing

The following object is masked from ‘package:tibble’:

    as_data_frame

The following objects are masked from ‘package:stats’:

    decompose, spectrum

The following object is masked from ‘package:base’:

    union

CO_OC_noun_adj_same_sent.after(x_even.after)



##########################################

CO_OC_noun_adj_following.before <- function(df){
  cooc <- cooccurrence(df$lemma, relevant = df$upos %in% c("NOUN", "ADJ"), skipgram = 1)
  head(cooc)
  
  wordnetwork <- head(cooc, 60)
  wordnetwork <- graph_from_data_frame(wordnetwork)
  ggraph(wordnetwork, layout = "fr") +
    geom_edge_link(aes(width = cooc, edge_alpha = cooc), edge_colour = "lightgreen") +
    geom_node_text(aes(label = name), col = "darkgreen", size = 4) +
    theme_graph(base_family = "Arial Narrow") +
    labs(title = "Words following one another: BEFORE", subtitle = "Nouns & Adjective")
}

CO_OC_noun_adj_following.after <- function(df){
  cooc <- cooccurrence(df$lemma, relevant = df$upos %in% c("NOUN", "ADJ"), skipgram = 1)
  head(cooc)
  
  wordnetwork <- head(cooc, 60)
  wordnetwork <- graph_from_data_frame(wordnetwork)
  ggraph(wordnetwork, layout = "fr") +
    geom_edge_link(aes(width = cooc, edge_alpha = cooc), edge_colour = "skyblue") +
    geom_node_text(aes(label = name), col = "darkblue", size = 4) +
    theme_graph(base_family = "Arial Narrow") +
    labs(title = "Words following one another: AFTER", subtitle = "Nouns & Adjective")
}


CO_OC_noun_adj_following.before(x_odd.before)

CO_OC_noun_adj_following.after(x_even.after)

Cooccurences (part 2)


Corrs <- function(df){
  df$id <- unique_identifier(df, fields = c("sentence_id", "doc_id"))
  dtm <- subset(df, upos %in% c("NOUN", "ADJ"))
  dtm <- document_term_frequencies(dtm, document = "id", term = "lemma")
  dtm <- document_term_matrix(dtm)
  dtm <- dtm_remove_lowfreq(dtm, minfreq = 5)
  termcorrelations <- dtm_cor(dtm)
  y <- as_cooccurrence(termcorrelations)
  y <- subset(y, term1 < term2 & abs(cooc) > 0.2)
  y <- y[order(abs(y$cooc), decreasing = TRUE), ]
  print(y[1:25,])
}


Corrs(x_odd.before)
Corrs(x_even.after)

Corrs(x_odd.before)

Corrs(x_even.after)

home

---
title: "Extrem(ist +) Fightin' Words"
author: "Breanna E. Green"
subtitle: practicing
output:
  html_document:
    toc: yes
    df_print: paged
  html_notebook:
    code_folding: show
    df_print: paged
    highlight: tango
    theme: united
    toc: yes
---

[home](https://bregreen.github.io/)

## load libraries

```{r, results='hide'}

### https://cran.r-project.org/web/packages/udpipe/vignettes/udpipe-usecase-postagging-lemmatisation.html
library(udpipe)
ud_model <- udpipe_download_model(language = "english")

library(tidyverse)
library(tidyr)
library(dplyr)
library(ggplot2)
library(ggrepel)
library(knitr)
library(tm)
library(quanteda)
library(lattice)
library(latticeExtra)
library(plotly)
library(pdp)
library(patchwork)

```

## load the FW functions

```{r load_fw_functions}

### CODE DIRECTLY FROM: https://burtmonroe.github.io/TextAsDataCourse/Tutorials/TADA-FightinWords.nb.html#

fwgroups <- function(dtm, groups, pair = NULL, weights = rep(1,nrow(dtm)), k.prior = .1) {
  
  weights[is.na(weights)] <- 0
  
  weights <- weights/mean(weights)
  
  zero.doc <- rowSums(dtm)==0 | weights==0
  zero.term <- colSums(dtm[!zero.doc,])==0
  
  dtm.nz <- apply(dtm[!zero.doc,!zero.term],2,"*", weights[!zero.doc])
  
  g.prior <- tcrossprod(rowSums(dtm.nz),colSums(dtm.nz))/sum(dtm.nz)
  
  # 
  
  g.posterior <- as.matrix(dtm.nz + k.prior*g.prior)
  
  groups <- groups[!zero.doc]
  groups <- droplevels(groups)
  
  g.adtm <- as.matrix(aggregate(x=g.posterior,by=list(groups=groups),FUN=sum)[,-1])
  rownames(g.adtm) <- levels(groups)
  
  g.ladtm <- log(g.adtm)
  
  g.delta <- t(scale( t(scale(g.ladtm, center=T, scale=F)), center=T, scale=F))
  
  g.adtm_w <- -sweep(g.adtm,1,rowSums(g.adtm)) # terms not w spoken by k
  g.adtm_k <- -sweep(g.adtm,2,colSums(g.adtm)) # w spoken by groups other than k
  g.adtm_kw <- sum(g.adtm) - g.adtm_w - g.adtm_k - g.adtm # total terms not w or k 
  
  g.se <- sqrt(1/g.adtm + 1/g.adtm_w + 1/g.adtm_k + 1/g.adtm_kw)
  
  g.zeta <- g.delta/g.se
  
  g.counts <- as.matrix(aggregate(x=dtm.nz, by = list(groups=groups), FUN=sum)[,-1])
  
  if (!is.null(pair)) {
    pr.delta <- t(scale( t(scale(g.ladtm[pair,], center = T, scale =F)), center=T, scale=F))
    pr.adtm_w <- -sweep(g.adtm[pair,],1,rowSums(g.adtm[pair,]))
    pr.adtm_k <- -sweep(g.adtm[pair,],2,colSums(g.adtm[pair,])) # w spoken by groups other than k
    pr.adtm_kw <- sum(g.adtm[pair,]) - pr.adtm_w - pr.adtm_k - g.adtm[pair,] # total terms not w or k
    pr.se <- sqrt(1/g.adtm[pair,] + 1/pr.adtm_w + 1/pr.adtm_k + 1/pr.adtm_kw)
    pr.zeta <- pr.delta/pr.se
    
    return(list(zeta=pr.zeta[1,], delta=pr.delta[1,],se=pr.se[1,], counts = colSums(dtm.nz), acounts = colSums(g.adtm)))
  } else {
    return(list(zeta=g.zeta,delta=g.delta,se=g.se,counts=g.counts,acounts=g.adtm))
  }
}

############## FIGHTIN' WORDS PLOTTING FUNCTION

# helper function
makeTransparent<-function(someColor, alpha=100)
{
  newColor<-col2rgb(someColor)
  apply(newColor, 2, function(curcoldata){rgb(red=curcoldata[1], green=curcoldata[2],
                                              blue=curcoldata[3],alpha=alpha, maxColorValue=255)})
}

fw.ggplot.groups <- function(fw.ch, groups.use = as.factor(rownames(fw.ch$zeta)), max.words = 50, max.countrank = 400, colorpalette=rep("black",length(groups.use)), sizescale=2, title="Comparison of Terms by Groups", subtitle = "", caption = "Group-specific terms are ordered by Fightin' Words statistic (Monroe, et al. 2008)") {
  if (is.null(dim(fw.ch$zeta))) {## two-group fw object consists of vectors, not matrices
    zetarankmat <- cbind(rank(-fw.ch$zeta),rank(fw.ch$zeta))
    colnames(zetarankmat) <- groups.use
    countrank <- rank(-(fw.ch$counts))
  } else {
    zetarankmat <- apply(-fw.ch$zeta[groups.use,],1,rank)
    countrank <- rank(-colSums(fw.ch$counts))
  }
  wideplotmat <- as_tibble(cbind(zetarankmat,countrank=countrank))
  wideplotmat$term=names(countrank)
  #rankplot <- gather(wideplotmat, party, zetarank, 1:ncol(zetarankmat))
  rankplot <- gather(wideplotmat, groups.use, zetarank, 1:ncol(zetarankmat))
  rankplot$plotsize <- sizescale*(50/(rankplot$zetarank))^(1/4)
  rankplot <- rankplot[rankplot$zetarank < max.words + 1 & rankplot$countrank<max.countrank+1,]
  rankplot$groups.use <- factor(rankplot$groups.use,levels=groups.use)
  
  p <- ggplot(rankplot, aes((nrow(rankplot)-countrank)^1, -(zetarank^1), colour=groups.use)) + 
    geom_point(show.legend=F,size=sizescale/2) + 
    theme_classic() +
    theme(axis.ticks=element_blank(), axis.text=element_blank() ) +
    ylim(-max.words,40) +
    facet_grid(groups.use ~ .) +
    geom_text_repel(aes(label = term), size = rankplot$plotsize, point.padding=.05,
                    box.padding = unit(0.20, "lines"), show.legend=F, max.overlaps = Inf) +
    scale_colour_manual(values = alpha(colorpalette, .7)) + 
#    labs(x="Terms used more frequently overall →", y="Terms used more frequently by group →",  title=title, subtitle=subtitle , caption = caption) 
    labs(x=paste("Terms used more frequently overall -->"), y=paste("Terms used more frequently by group -->"),  title=title, subtitle=subtitle , caption = caption) 
  
}

options(ggrepel.max.overlaps = Inf)

fw.keys <- function(fw.ch,n.keys=10) {
  n.groups <- nrow(fw.ch$zeta)
  keys <- matrix("",n.keys,n.groups)
  colnames(keys) <- rownames(fw.ch$zeta)
  
  for (g in 1:n.groups) {
    keys[,g] <- names(sort(fw.ch$zeta[g,],dec=T)[1:n.keys])
  }
  keys
}
```


## Compare Associated Press 1994-2010: Before and After "extremist" and other query terms

**Query search:**
(activi* | ahbash | akromiya | anjem | ansharut | anticapital* | antidemocr* | antiestablish* | antifa | antigovern* | antimilitar* | antimonarch* | antipatri* | antireli* | antisem* | antisocia* | antisyst* | apost* | atharis | athei* | atheists | außerparlamentari* | authoritar* | bagau* | bigots | bplf | bukhari* | capitulatio* | conspirato* | counterj* | cybercalip* | damigo | dawro* | demon* | deradicaliza* | deviatio* | diqqi | dissid* | djamaat | dotbus* | ecofas* | espou* | ethnonationa* | extrem* | facists | fadaia* | fanat* | fasci* | fetö | fightdem* | freedo* | fundamental* | fuqra | gafatar | gamerga* | gemidzii | ghuluww | globali* | gramsc* | gülen* | hacktiv* | haquna | hardline | harkatul | hatemon* | heimwe* | hezbol* | hinduph* | hindutva | hizbut | hojja* | ideolog* | incitem* | inciters | insurr* | intacti* | islam4uk | islam* | jaljalat | jbakc | jihadi* | jmjb | jrtn | judai* | juhayman | jundu* | kadiza* | kahanism | kahanist | karram* | kaysa* | khalis* | khatmia | khawarij | khomein* | koutla | leftist | leftists | leftwing | liberatio* | madkha* | madkhal* | maimonid* | manosp* | mauras* | mcln | militan* | millatu | monarc* | mudja* | muhaji* | mujahid* | murab* | muttahi* | najjadah | nationali* | neofas* | neona* | opantish | oppositi* | paleolibertar* | paramili* | parliamenta* | pegida | populist | principa* | profe* | prowar | putinist | qadari | quranism | quranist | qutbism | qutbist | qutbists | racis* | radica* | reactioni* | reformis* | reichsbürgerbewe* | rightist | rightw* | rofiq | russoph* | sabireen | sadda* | salaf* | sayaff | scriptura* | secula* | separationi* | sharia4hol* | sikrikim | split* | squadism | strasse* | subver* | suidlan* | sukarn* | suprema* | supremac* | sympathi* | table* | tabliq | takfir | takfir* | takfi* |terror* | theoc* | titoite | triba* | trots* | trotsk* | ukrainoph* | ultraconserva* | ultralib* | ultranationa* | ultrar* | uscmo | wahabbi | wahab* | wahha* | xenoph* | yulde* | zinovie*)


**Load and clean the data**

  * to string & lower text
  * pivot to long format
  * apply text_cleaner to one column "context.text"

```{r,  results='asis'}
text_cleaner<-function(corpus){
  tempcorpus<-Corpus(VectorSource(corpus))
  tempcorpus<-tm_map(tempcorpus,
                    removePunctuation)
  tempcorpus<-tm_map(tempcorpus,
                    stripWhitespace)
  tempcorpus<-tm_map(tempcorpus,
                    removeNumbers)
  tempcorpus<-tm_map(tempcorpus,
                     removeWords, stopwords("english"))
  tempcorpus<-tm_map(tempcorpus, 
                    stemDocument)
  return(tempcorpus)
}

```

```{r, echo=FALSE, results= FALSE}

extrem_NYT.dfm_all <-read.delim("~/Documents/GitHub/70corr_extremist_9410_NYT.txt", header=TRUE, sep="\t")
extrem_NYT.dfm_all$pubdate = substr(extrem_NYT.dfm_all$Text.ID,9,16)
extrem_NYT.dfm_all$pubdate <- as.POSIXct(extrem_NYT.dfm_all$pubdate, format = "%Y%m%d")

####################################################################

set.seed(2217)
extrem_NYT.dfm <- as.data.frame(sample_n(extrem_NYT.dfm_all, 15000))
rm(extrem_NYT.dfm_all)

####################################################################

# extrem_NYT.dfm$Context.before = lapply(extrem_NYT.dfm$Context.before, toString)
# extrem_NYT.dfm$Context.before = lapply(extrem_NYT.dfm$Context.before, tolower)
# 
# extrem_NYT.dfm$Context.after = lapply(extrem_NYT.dfm$Context.after, toString)
# extrem_NYT.dfm$Context.after = lapply(extrem_NYT.dfm$Context.after, tolower)


####################################################################

extrem_NYT.dfm <- extrem_NYT.dfm %>% distinct(Context.before, .keep_all = TRUE)

extrem_NYT.dfm.long <- pivot_longer(extrem_NYT.dfm, cols=c(Context.before, Context.after), names_to = "Context", values_to = "context.text")

extrem_NYT.dfm.long$Context <- as.factor(extrem_NYT.dfm.long$Context)


####################################################################

extremecorpus <-text_cleaner(extrem_NYT.dfm.long$context.text)

```


Calculate FW.

```{r, message=FALSE}

e <- dfm(extremecorpus$content)
message(dim(e))
head(e)

#############################################

e <- dfm_select(e, pattern = stopwords("english"), selection = "remove")
e <- dfm_select(e, min_nchar = 2)
e <- dfm_trim(e, min_termfreq = 4, min_docfreq = .05, verbose=TRUE)

#dim(e)
# sparsity(e)

#############################################

extrem_dtm <- convert(e, to='data.frame')
extrem_dtm <- extrem_dtm[-c(1)]
w <- which( sapply(extrem_dtm, class ) == 'character' )

#############################################

fw.extrem <- fwgroups(extrem_dtm, groups=extrem_NYT.dfm.long$Context)

rm(extrem_dtm)

```


**Get and show the top words per group by zeta.**

```{r echo=TRUE, results="asis"}

fwkeys.extrem <- fw.keys(fw.extrem, n.keys=20)
cols <- rev(colnames(fwkeys.extrem))
fwkeys.extrem <- fwkeys.extrem[,cols]
kable(fwkeys.extrem)

```

Plot: Before in Blue, After in Red

```{r, fig.height=5, fig.width=4}

p.fw.extrem <- fw.ggplot.groups(fw.extrem,sizescale=4,max.words=200,
                                max.countrank=400,colorpalette=c("red","blue"),
                                title = 'Comparison of Terms Before and After Query Word')
p.fw.extrem
```

## Calculate by query item/search term

```{r, message=FALSE, fig.height=8, fig.width=4}

extrem_NYT.dfm.long$Query.item <- as.factor(extrem_NYT.dfm.long$Query.item)

top_n <-as.data.frame(sort(table(extrem_NYT.dfm.long$Query.item), decreasing = TRUE)[1:5]) 
message(dim(top_n))
colnames(top_n) <- c('term', 'Freq')
message(top_n)

extrem_dtm_topn <- convert(e, to='data.frame')
extrem_dtm_topn$Number.of.hit <- extrem_NYT.dfm.long$Number.of.hit

topn_terms <- extrem_NYT.dfm.long %>%
      filter(Query.item %in% top_n$term)

extrem_dtm_topn_keep <- extrem_dtm_topn %>% 
    filter(Number.of.hit %in% topn_terms$Number.of.hit)

r <- sum(length(extrem_dtm_topn_keep))

extrem_dtm_topn_keep <- extrem_dtm_topn_keep[-c(1, r)]

fw.query_item <- fwgroups(extrem_dtm_topn_keep,groups = topn_terms$Query.item)
fwkeys.query_item <- fw.keys(fw.query_item, n.keys=15)
kable(fwkeys.query_item)

rm(r)
rm(extrem_dtm_topn)

########################################################

p.fw.query_item <- fw.ggplot.groups(fw.query_item,sizescale=3.2,max.words=150,max.countrank=400,
                                    colorpalette=c("darkgreen","darkgreen","darkgreen","darkgreen","darkgreen"),
                                    title = 'Comparison of Terms by Overall Top Terms')
p.fw.query_item

```



```{r, message=FALSE, fig.height=5, fig.width=4}

extrem_dtm_topn <- convert(e, to='data.frame')
extrem_dtm_topn$Number.of.hit <- extrem_NYT.dfm.long$Number.of.hit
extrem_dtm_topn$Context <- extrem_NYT.dfm.long$Context

topn_terms <- extrem_NYT.dfm.long %>%
      filter(Query.item %in% top_n$term)

extrem_dtm_topn_keep <- extrem_dtm_topn %>% 
    filter(Number.of.hit %in% topn_terms$Number.of.hit)

extrem_dtm_topn_keep_before <- extrem_dtm_topn_keep[grep("before",extrem_dtm_topn_keep$Context),]
extrem_dtm_topn_keep_after <- extrem_dtm_topn_keep[grep("after", extrem_dtm_topn_keep$Context),]

rr <-dim(topn_terms)[1]
r <- sum(length(extrem_dtm_topn_keep_before))
topn_terms_before <- topn_terms[seq(1,rr,2),]
topn_terms_after <- topn_terms[seq(2,rr,2),]

extrem_dtm_topn_keep_before <- extrem_dtm_topn_keep_before[-c(1, r-1, r)]
extrem_dtm_topn_keep_after <- extrem_dtm_topn_keep_after[-c(1, r-1, r)]

rm(rr)
rm(r)
rm(extrem_dtm_topn)

#############################################
fw.query_item_before <- fwgroups(extrem_dtm_topn_keep_before,groups = topn_terms_before$Query.item)
fwkeys.query_item_before <- fw.keys(fw.query_item_before, n.keys=15)
kable(fwkeys.query_item_before, caption = "Top 15 Words for Query Term: BEFORE")

p.fw.query_item_before <- fw.ggplot.groups(fw.query_item_before,sizescale=2,max.words=150,max.countrank=400,
                                           colorpalette = c('blue','blue','blue', 'blue','blue'),
                                           title = 'Comparison of Terms by Overall Top Terms: BEFORE')
p.fw.query_item_before

#############################################
fw.query_item_after <- fwgroups(extrem_dtm_topn_keep_after,groups = topn_terms_after$Query.item)
fwkeys.query_item_after <- fw.keys(fw.query_item_after, n.keys=15)
kable(fwkeys.query_item_after, caption = "Top 15 Words for Query Term: AFTER")

p.fw.query_item_after <- fw.ggplot.groups(fw.query_item_after,sizescale=2,max.words=150,max.countrank=400,
                                           colorpalette = c('red', 'red','red','red','red'),
                                          title = 'Comparison of Terms by Overall Top Terms: AFTER')
p.fw.query_item_after


```



## Calculate Parts of speech by before and after

Calculate FW and keys
```{r, results='hide', warning=FALSE}

ud_model <- udpipe_load_model(ud_model$file_model)

txt <-as.character(extrem_NYT.dfm.long$context.text)

x_udp <- udpipe_annotate(ud_model, x = txt, doc_id = seq_along(txt))
x <- as.data.frame(x_udp)

x$doc_id <-as.integer(x$doc_id)

x_odd.before <- x[x$doc_id %% 2 == 1,]
x_even.after <-x[x$doc_id %% 2 == 0, ]

```


*A few barchart functions*

```{r, results='hide'}

## UNIVERSAL PoS
UPOS_barchart <- function(df1, df2){
  stats1 <- txt_freq(df1$upos)
  stats1$key <- factor(stats1$key, levels = rev(stats1$key))
  
  stats2 <- txt_freq(df2$upos)
  stats2$key <- factor(stats2$key, levels = rev(stats2$key))
  
  c(barchart(key ~ freq, data = stats1, col = "cadetblue", 
        main = "UPOS (Universal Parts of Speech)\n frequency of occurrence: BEFORE vs AFTER", 
         xlab = "Freq"), 
    barchart(key ~ freq, data = stats2, col =  'skyblue',
         xlab = "Freq"))
}



## NOUNS
NOUNS_barchart <- function(df1, df2){
  
  stats1 <- subset(df1, upos %in% c("NOUN")) 
  stats1 <- txt_freq(stats1$token)
  stats1$key <- factor(stats1$key, levels = rev(stats1$key))
  
  stats2 <- subset(df2, upos %in% c("NOUN")) 
  stats2 <- txt_freq(stats2$token)
  stats2$key <- factor(stats2$key, levels = rev(stats2$key))
  
  c(barchart(key ~ freq, data = head(stats1, 20), col = "cadetblue", 
           main = "Most occurring nouns: BEFORE vs AFTER", xlab = "Freq"),
      barchart(key ~ freq, data = head(stats2, 20), col = "skyblue", 
            xlab = "Freq"))
}

## ADJECTIVES
ADJ_barchart <- function(df1, df2){
  
  stats1 <- subset(df1, upos %in% c("ADJ")) 
  stats1 <- txt_freq(stats1$token)
  stats1$key <- factor(stats1$key, levels = rev(stats1$key))
  
  stats2 <- subset(df2, upos %in% c("ADJ")) 
  stats2 <- txt_freq(stats2$token)
  stats2$key <- factor(stats2$key, levels = rev(stats2$key))
  
  c(barchart(key ~ freq, data = head(stats1, 20), col = "cadetblue", 
           main = "Most occurring adjectives: BEFORE vs AFTER", xlab = "Freq"),
      barchart(key ~ freq, data = head(stats2, 20), col = "skyblue", 
         xlab = "Freq"))
}

## Using RAKE to find keywords
RAKE_KW_barchart <- function(df1,df2){
  
  stats1 <- keywords_rake(x = df1, term = "lemma", group = "doc_id", 
                         relevant = df1$upos %in% c("NOUN", "ADJ"))
  stats1$key <- factor(stats1$keyword, levels = rev(stats1$keyword))
  
  stats2 <- keywords_rake(x = df2, term = "lemma", group = "doc_id", 
                         relevant = df2$upos %in% c("NOUN", "ADJ"))
  stats2$key <- factor(stats2$keyword, levels = rev(stats2$keyword))
  
  
  c(barchart(key ~ rake, data = head(subset(stats1, freq > 3), 20), col = "cadetblue", 
           main = "Keywords identified by RAKE: BEFORE vs AFTER", 
           xlab = "Rake"),
    barchart(key ~ rake, data = head(subset(stats2, freq > 3), 20), col = "skyblue", 
           xlab = "Rake"))
}

## Using Pointwise Mutual Information Collocations
PWI_barchart <- function(df1, df2){
  
  df1$word <- tolower(df1$token)
  stats1 <- keywords_collocation(x = df1, term = "word", group = "doc_id")
  stats1$key <- factor(stats1$keyword, levels = rev(stats1$keyword))
  
  df2$word <- tolower(df2$token)
  stats2 <- keywords_collocation(x = df2, term = "word", group = "doc_id")
  stats2$key <- factor(stats2$keyword, levels = rev(stats2$keyword))
  
  c(barchart(key ~ pmi, data = head(subset(stats1, freq > 3), 20), col = "cadetblue", 
           main = "Keywords identified by PMI Collocation: BEFORE vs AFTER", 
           xlab = "PMI (Pointwise Mutual Information)"),
      barchart(key ~ pmi, data = head(subset(stats2, freq > 3), 20), col = "skyblue", 
           xlab = "PMI (Pointwise Mutual Information)"))
}

## Using a sequence of POS tags (noun phrases / verb phrases)
POS_barchart <- function(df1, df2){
  
  df1$phrase_tag <- as_phrasemachine(df1$upos, type = "upos")
  stats1 <- keywords_phrases(x = df1$phrase_tag, term = tolower(df1$token), 
                            pattern = "(A|N)*N(P+D*(A|N)*N)*", 
                            is_regex = TRUE, detailed = FALSE)
  stats1 <- subset(stats1, ngram > 1 & freq > 3)
  stats1$key <- factor(stats1$keyword, levels = rev(stats1$keyword))
  
  df2$phrase_tag <- as_phrasemachine(df2$upos, type = "upos")
  stats2 <- keywords_phrases(x = df2$phrase_tag, term = tolower(df2$token), 
                            pattern = "(A|N)*N(P+D*(A|N)*N)*", 
                            is_regex = TRUE, detailed = FALSE)
  stats2 <- subset(stats2, ngram > 1 & freq > 3)
  stats2$key <- factor(stats2$keyword, levels = rev(stats2$keyword))
  
  c(barchart(key ~ freq, data = head(stats1, 20), col = "cadetblue", 
           main = "Keywords - simple noun phrases: BEFORE vs AFTER", xlab = "Frequency"),
      barchart(key ~ freq, data = head(stats2, 20), col = "skyblue", 
               xlab = "Frequency"))
}
```


## Bar Charts from Functions Above

```{r}

UPOS_barchart(x_odd.before, x_even.after)
NOUNS_barchart(x_odd.before, x_even.after)
ADJ_barchart(x_odd.before, x_even.after)
RAKE_KW_barchart(x_odd.before, x_even.after)
PWI_barchart(x_odd.before, x_even.after)
POS_barchart(x_odd.before, x_even.after)

```


## Cooccurences

```{r}

CO_OC_noun_adj_same_sent.before <- function(df1){
  
  library(igraph)
  library(ggraph)
  library(ggplot2)
  
  cooc <- cooccurrence(x = subset(df1, upos %in% c("NOUN", "ADJ")), 
                       term = "lemma", 
                       group = c("doc_id", "paragraph_id", "sentence_id"))

  wordnetwork <- head(cooc, 60)
  wordnetwork <- graph_from_data_frame(wordnetwork)
  
  ggraph(wordnetwork, layout = "fr") +
    geom_edge_link(aes(width = cooc, edge_alpha = cooc), edge_colour = "pink") +
    geom_node_text(aes(label = name), col = "darkgreen", size = 4) +
    theme_graph(base_family = "Arial Narrow") +
    theme(legend.position = "none") +
    labs(title = "Cooccurrences within sentence: BEFORE", subtitle = "Nouns & Adjective")
  
}

CO_OC_noun_adj_same_sent.after <- function(df2){
  
  library(igraph)
  library(ggraph)
  library(ggplot2)
  
  cooc <- cooccurrence(x = subset(df2, upos %in% c("NOUN", "ADJ")), 
                       term = "lemma", 
                       group = c("doc_id", "paragraph_id", "sentence_id"))

  wordnetwork <- head(cooc, 60)
  wordnetwork <- graph_from_data_frame(wordnetwork)
  
  ggraph(wordnetwork, layout = "fr") +
    geom_edge_link(aes(width = cooc, edge_alpha = cooc), edge_colour = "lightgreen") +
    geom_node_text(aes(label = name), col = "darkblue", size = 4) +
    theme_graph(base_family = "Arial Narrow") +
    theme(legend.position = "none") +
    labs(title = "Cooccurrences within sentence: AFTER", subtitle = "Nouns & Adjective")
  
}


CO_OC_noun_adj_same_sent.before(x_odd.before)
CO_OC_noun_adj_same_sent.after(x_even.after)


##########################################

CO_OC_noun_adj_following.before <- function(df){
  cooc <- cooccurrence(df$lemma, relevant = df$upos %in% c("NOUN", "ADJ"), skipgram = 1)
  head(cooc)
  
  wordnetwork <- head(cooc, 60)
  wordnetwork <- graph_from_data_frame(wordnetwork)
  ggraph(wordnetwork, layout = "fr") +
    geom_edge_link(aes(width = cooc, edge_alpha = cooc), edge_colour = "lightgreen") +
    geom_node_text(aes(label = name), col = "darkgreen", size = 4) +
    theme_graph(base_family = "Arial Narrow") +
    labs(title = "Words following one another: BEFORE", subtitle = "Nouns & Adjective")
}

CO_OC_noun_adj_following.after <- function(df){
  cooc <- cooccurrence(df$lemma, relevant = df$upos %in% c("NOUN", "ADJ"), skipgram = 1)
  head(cooc)
  
  wordnetwork <- head(cooc, 60)
  wordnetwork <- graph_from_data_frame(wordnetwork)
  ggraph(wordnetwork, layout = "fr") +
    geom_edge_link(aes(width = cooc, edge_alpha = cooc), edge_colour = "skyblue") +
    geom_node_text(aes(label = name), col = "darkblue", size = 4) +
    theme_graph(base_family = "Arial Narrow") +
    labs(title = "Words following one another: AFTER", subtitle = "Nouns & Adjective")
}


CO_OC_noun_adj_following.before(x_odd.before)
CO_OC_noun_adj_following.after(x_even.after)

```

## Cooccurences (part 2)
```{r}

Corrs <- function(df){
  df$id <- unique_identifier(df, fields = c("sentence_id", "doc_id"))
  dtm <- subset(df, upos %in% c("NOUN", "ADJ"))
  dtm <- document_term_frequencies(dtm, document = "id", term = "lemma")
  dtm <- document_term_matrix(dtm)
  dtm <- dtm_remove_lowfreq(dtm, minfreq = 5)
  termcorrelations <- dtm_cor(dtm)
  y <- as_cooccurrence(termcorrelations)
  y <- subset(y, term1 < term2 & abs(cooc) > 0.2)
  y <- y[order(abs(y$cooc), decreasing = TRUE), ]
  print(y[1:25,])
}

```
```{r corrs}

Corrs(x_odd.before)

Corrs(x_even.after)

```

```{r final}
```





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